Abstract

After several decades in service, the deterioration of storm-water pipe assets is inevitable. The deterioration of storm-water
pipes is characterized by structural deterioration and hydraulic deterioration. Condition assessment using closed circuit television (CCTV)
inspection is often carried out to assess the structural condition of pipes. However, the knowledge on the condition of storm-water pipe
assets is still limited for strategic planning of maintenance and rehabilitation, because generally only a small sample is CCTV-inspected
and in almost all cases, these pipes are inspected once only due to high costs. The challenge for researchers is to use the sample of
CCTV-inspected pipes for developing mathematical models that can predict the structural condition of remaining pipes as well as the
future condition of pipes. In this present study, the deterioration pattern of storm-water pipes is constructed on the basis that each pipe has
its own deterioration rate due to its pipe factors. Based on this, two mathematical models using multiple logistic regression �MLR� and
probabilistic neural networks �PNN� are developed for predicting the structural condition of individual pipes. The MLR model was
calibrated using the maximum likelihood method and the PNN model was trained using a genetic algorithm �GA�. The predictive
performances of both models were compared using CCTV data collected for a local government authority in Melbourne, Australia. The
results showed that the PNN model was more suited for modeling the structural deterioration of individual storm-water pipes than the
MLR model. Furthermore, the use of GA improved the training results of the PNN model compared to the trial and error method.